A Customized Neural Network for Sensor Fusion in On-Line Monitoring of Cutting Tool Wear

[+] Author and Article Information
Choon Seong Leem

Dept. of Industrial Engineering, Rutgers University, Piscataway, NJ

D. A. Dornfeld

Dept. of Mechanical Engineering, University of California at Berkeley, Berkeley, CA

S. E. Dreyfus

Dept. of Industrial Engineering and Operations Research, University of California at Berkeley, Berkeley, CA

J. Eng. Ind 117(2), 152-159 (May 01, 1995) (8 pages) doi:10.1115/1.2803289 History: Received April 01, 1993; Revised March 01, 1994; Online January 17, 2008


A customized neural network for sensor fusion of acoustic emission and force in on-line detection of tool wear is developed. Based on two critical concerns regarding practical and reliable tool-wear monitoring systems, the maximal utilization of “unsupervised” sensor data and the avoidance of off-line feature analysis, the neural network is trained by unsupervised Kohonen’s Feature Map procedure followed by an Input Feature Scaling algorithm. After levels of tool wear are topologically ordered by Kohonen’s Feature Map, input features of AE and force sensor signals are transformed via Input Feature Scaling so that the resulting decision boundaries of the neural network approximate those of error-minimizing Bayes classifier. In a machining experiment, the customized neural network achieved high accuracy rates in the classification of levels of tool wear. Also, the neural network shows several practical and reliable properties for the implementation of the monitoring system in manufacturing industries.

Copyright © 1995 by The American Society of Mechanical Engineers
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